One of the inherent challenges of digital marketing is presenting performance data in a way that’s easy for stakeholders, regardless of their background, to understand without having to spend hours building manual reports; and there’s no shortage of applications trying to solve the problem. I’ve tried Cyfe and Klipfolio, as well as the custom reporting features built in to Google Analytics, MOZ and HubSpot, but I have yet to find something with the flexibility required to become a go-to solution. As a result, I was quite interested when Google jumped into the fray with Google Data Studio.
Soon after it was announced that Google Data Studio was being made available for free, I identified a potential test case in a client for whom we had been producing manual monthly reports on lead generation KPIs for over a year. Each report took over an hour to build, and even then I didn’t feel like we were adequately conveying the meaning and significance of the data; so on the first day of June, I logged in to Data Studio to see if I couldn’t produce a better May report, faster.
‘Faster,’ of course, is easy to quantify, but ‘better’ is subjective, so let me start by outlining what I look for in a tool like this:
- design – the visualizations have to look professional without requiring a designer’s involvement or work in an external graphics application.
- flexibility – lots of built-in integrations and, preferably, the ability to ‘roll your own’.
- internal ease-of-use – While I’ve been doing digital marketing for over 20 years, I am by no means a data scientist or software engineer, so if getting what I’m looking for involves writing SQL queries or anything beyond basic markup it’s probably not worth the trouble.
- client ease-of-use – the end-user (client) of a tool like this is often very busy but not very technical, so the closer it can get to zero learning curve, the better for all parties.
- stability – if the data connections break or other critical elements become unavailable often, the risk of wasting my and my clients’ time cancels out any other benefits
- ROI – I certainly don’t mind paying for a tool that’s clearly helping our client and helping us land and/or retain business, but if it’s not doing either, I’ll start to mind pretty quickly.
In terms of raw data, this account is a dream. We measure every user interaction from first click to close, and we have an accurate fix on average lifetime value per client (LTV), all of which allows us to report on the entire acquisition funnel as well as financial metrics like projected revenue by source and marketing return on investment (MROI). The only downside was that we hadn’t been able to automate the reports the data to visualize the funnel report and calculate financial metrics comes from different sources: specifically, visitors and visitor-to-lead conversions comes from Google Analytics, lead-to-client conversions from a hosted chat application called NGAGE Live, and revenue-related metrics from both static data and NGAGE.
Google Data Studio Integrations
As the biggest roadblock to automated reporting facing this client, I checked out Data Studio’s capabilities in that area first, and here’s what I found. In Data Studio, integration is achieved using data connectors, and connectors for Google properties require just a few clicks to set up. As of this writing, the following connectors are available:
- Analytics and Analytics 360
- Search Console
- Attribution 360
- Cloud SQL/PostgreSQL
- YouTube Channel
Currently, the only way to integrate data from non-Google sources is by moving it into an instance Google Sheets or Cloud MySQL/PostgreSQL that’s connected to Data Studio. While that was fine for a first run with a free tool that’s still in Beta, I would love to see the list expanded over time.
Adding a Google Source to Data Studio
Adding the Google Analytics data was just a matter of providing credentials and selecting the account I wanted to integrate. Once that was done, including it in the report was a simple matter of selecting the data point you want and configuring how I would like it to display.
If you’ve got functional knowledge of Google Analytics and spreadsheets like Excel or Google Sheets, you shouldn’t have any trouble with this part – and in some cases, all of your data will come from Analytics or another plug-and-play source and you’ll be ready to play with all the cool ways Google Data Studio allows you manipulate and display it (more on that below).
Adding a non-Google Source to Data Studio
This is where the dream of producing a fully-automated Data Studio dashboard for this client met its untimely demise, as doing so would require access to the NGAGE reporting database and a mechanism for moving data from there to Sheets or Cloud MySQL/PostgreSQL. So, for now, the best option is to manually download a CSV from NGAGE and paste it into a Google Sheet that’s connected to our Data Studio report. It’s not a perfect solution, but if it’s the only manual step, it’s still a lot less work than the completely manual approach we had been taking.
Creating a Conversion Funnel from Multiple Data Sources
The platform does have its limitations, but the ability to combine data from multiple and tons of flexibility in how it’s presented make Google Data Studio a standout among its peers. Case in point, I was able to create a funnel visualization using data from two different sources in just a few minutes.
The simple three-stage funnel pulls site visitors from Google Analytics, and leads as well as clients from NGAGE. Since all I needed for each field was the raw output, I selected “scorecard” as the chart type, but as you’ll see below, users can pick from a wide array of display options.
Working with Calculated Fields
As mentioned above, we’re fortunate enough to know that, on average, each prospect that our client closes is worth about $4,000 in revenue. Given that as a static data point, we can create a new field containing the output of “number of clients (already displayed at the bottom of the funnel diagram) * $4,000.” Once we’ve done that, all we have to do is select the new field and add the new metric to the dashboard as a scorecard.
Data Studio calls the field we created a calculated field. Put simply, a calculated field is a field created by applying a formula to an existing field. In addition to displaying the output of a formula, calculated fields allow you to extract or transform text and return data based on logical comparisons, and you can even create new calculated fields with the data from existing ones, which gives the feature an endless array of potential applications.
Displaying Geographical Data
One of this client’s goals for this year is expanding into a new geographic area, so I thought I would drop in a map indicating the location of each visitor that converted to a lead.
As had been the case with site visitors, displaying lead location was a matter of a few clicks. I simply chose the “Geo Map” chart type, placed it on the page, and selected Google Analytics as the data source, City as the dimension, and Chat Leads as the metric, then I set the zoom level to “United States,” and that was that.
Other Chart Types
While displaying the data I’ve described to this point would be sufficient for this client, I was having fun, so I added a second page that shows visitors, leads, and clients on both per-day and cumulative time series charts. I won’t belabor the process of building those, as it uses the same concepts that I described above, but it is useful as an example of the multitude of ways Google Data Studio allows you to display the same data.
Styling the Google Data Studio Dashboard
If you’re at all design-inclined, this may be the most fun aspect of using Data Studio, as it’s quite flexible and open-ended. When a chart on the page is selected, the contextual menu on the right side of the page contains properties settings in two tabs: one labeled “Data” and “Style.”
The data tab contains settings such as Chart Type, Data Source, Dimensions, Date Range and Filter, the “Style” tab contains settings for the line color used in charts, a selector for line and bar chart types, a cumulative toggle, options to show points, data labels, trendlines, and a bunch of other settings that allow a surprisingly granular level of control.
In addition the chart style settings, there are tools for drawing shapes and inserting text, along with all the style settings I mentioned above; as well as the ability to upload and insert images.
I took advantage of these features to place the (redacted) client logo in the top left of the page, format the outline and background of the date picker, add labels and create boxes to separate the various visualizations. If you’ve used Google Slides, PowerPoint, Keynote or Visio before, you should be right at home with this easy-to-use interface.
After spending a total of about six hours in Google Data Studio here’s my read on how it fared against the criteria outlined above:
- design – what you lose in preformatted layouts, you gain in flexibility. As a quasi-designer, I found this to be a big plus.
- flexibility – per above, lots of design flexibility, and the SQL options appear to offer flexibility to users with SQL chops.
- internal ease-of-use – even though I jumped right in with no training, there wasn’t much of a learning curve. The one thing that stumped me was calculating lead-to-client conversion rate, which I ended up learning couldn’t be done because calculated fields can’t reference two data sources. I feel like being able to do so is a pretty baseline requirement for an application of this nature, so hopefully, the Data Studio team will get it added soon.
- client ease-of-use – immediately after sending our client the link, I received a reply saying that it’s much easier to understand than the old reports, and if he’s happy, I’m happy.
- stability – there was a point at which I thought I had successfully hacked the conversion rate calculation referenced above. In fact, I even had it working, but when I loaded the report the following day my hack had managed to break, not only the conversion calculations but the whole dashboard – which I ended up having to rebuild.
- ROI – being a free product, the bar is pretty low here, but considering the fact that it will save me a lot of time and better demonstrate our successes to our clients moving forward, I’d favor Google Data Studio over all of the paid products I’ve used.
In summary, Google Data Studio did not disappoint. I found it fast, responsive, flexible and easy to use; and while I’ve just scratched the surface, I can see it becoming an valuable tool for those of us charged with cutting through the data smog to reveal clear, actionable insights to our clients and stakeholders.